docstr: Predict whether the most recent window of data has drifted from the reference data.
  
  Parameters
  ----------
  x_t
      A single instance to be added to the test-window.
  return_test_stat
      Whether to return the test statistic (LSDD) and threshold.
  
  Returns
  -------
  Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
      - ``'meta'`` has the model's metadata.
      - ``'data'`` contains the drift prediction and optionally the test-statistic and threshold.
docstr_tok: ["Predict","whether","the","most","recent","window","of","data","has","drifted","from","the","reference","data",".","Parameters","--","--","--","--","--","x_t","A","single","instance","to","be","added","to","the","test-window",".","return_test_stat","Whether","to","return","the","test","statistic","(","LSDD",")","and","threshold",".","Returns","--","--","--","-","Dictionary","containing","``","'meta","'","``","and","``","'data","'","``","dictionaries",".","-","``","'meta","'","``","has","the","model","'s","metadata",".","-","``","'data","'","``","contains","the","drift","prediction","and","optionally","the","test-statistic","and","threshold","."]
code: def predict(
      self, x_t: Union[np.ndarray, Any], return_test_stat: bool = True
  ) -> Dict[Dict[str, str], Dict[str, Union[int, float]]]:
      """
      Predict whether the most recent window of data has drifted from the reference data.
  
      Parameters
      ----------
      x_t
          A single instance to be added to the test-window.
      return_test_stat
          Whether to return the test statistic (LSDD) and threshold.
  
      Returns
      -------
      Dictionary containing ``'meta'`` and ``'data'`` dictionaries.
          - ``'meta'`` has the model's metadata.
          - ``'data'`` contains the drift prediction and optionally the test-statistic and threshold.
      """
      return self._detector.predict(x_t, return_test_stat)
  
file_path: alibi-detect/alibi_detect/cd/lsdd_online.py
imports: import os
  import numpy
  from typing import Any, Callable, Dict, Optional, Union
  from alibi_detect.utils.frameworks import has_pytorch, has_tensorflow, BackendValidator, Framework
  from alibi_detect.base import DriftConfigMixin
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 6 for calling the LSDDDriftOnline obj's underlying member method code with example usage: obj.predict(x_t, return_test_stat) and returns: self